Conventional content recommendation systems for users accessing a document can be classified into content-based systems and collaborative filtering systems. In content-based filtering systems, content recommendations are pre-computed and thus are static in manner. That is, documents that may be related or of interest to a currently viewed document are typically determined offline and provided to a user when the user visits a site. The recommendations do not take a user's preferences into consideration.
Collaborative filtering systems take into consideration what other users find to be relevant. For example, for each piece of content, a set of other content that other users also viewed is suggested. Unfortunately, what one user considers to be relevant may not be considered relevant by another user.
A combination of both content-based and collaborative filtering systems may also be conventionally used. In these systems, content may be ranked based on factors such as hyperlinks between content, query language, geographical location of the users, and so forth.